{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(30, 200, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 30.        ,  35.86206897,  41.72413793,  47.5862069 ,\n",
       "        53.44827586,  59.31034483,  65.17241379,  71.03448276,\n",
       "        76.89655172,  82.75862069,  88.62068966,  94.48275862,\n",
       "       100.34482759, 106.20689655, 112.06896552, 117.93103448,\n",
       "       123.79310345, 129.65517241, 135.51724138, 141.37931034,\n",
       "       147.24137931, 153.10344828, 158.96551724, 164.82758621,\n",
       "       170.68965517, 176.55172414, 182.4137931 , 188.27586207,\n",
       "       194.13793103, 200.        ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 3 * x + np.random.randn(30) + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20c4a0b10f0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import layers\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential()\n",
    "model.add(layers.Dense(1, input_dim=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_2 (Dense)              (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='mse'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "30/30 [==============================] - 0s 231us/step - loss: 29207.9043\n",
      "Epoch 2/2000\n",
      "30/30 [==============================] - 0s 271us/step - loss: 29168.5547\n",
      "Epoch 3/2000\n",
      "30/30 [==============================] - 0s 264us/step - loss: 29127.9746\n",
      "Epoch 4/2000\n",
      "30/30 [==============================] - 0s 246us/step - loss: 29086.6367\n",
      "Epoch 5/2000\n",
      "30/30 [==============================] - 0s 233us/step - loss: 29044.7754\n",
      "Epoch 6/2000\n",
      "30/30 [==============================] - 0s 235us/step - loss: 29002.5566\n",
      "Epoch 7/2000\n",
      "30/30 [==============================] - 0s 233us/step - loss: 28960.0723\n",
      "Epoch 8/2000\n",
      "30/30 [==============================] - 0s 233us/step - loss: 28917.4043\n",
      "Epoch 9/2000\n",
      "30/30 [==============================] - 0s 233us/step - loss: 28874.5879\n",
      "Epoch 10/2000\n",
      "30/30 [==============================] - 0s 234us/step - loss: 28831.6777\n",
      "Epoch 11/2000\n",
      "30/30 [==============================] - 0s 229us/step - loss: 28788.6875\n",
      "Epoch 12/2000\n",
      "30/30 [==============================] - 0s 233us/step - loss: 28745.6367\n",
      "Epoch 13/2000\n",
      "30/30 [==============================] - 0s 234us/step - loss: 28702.5586\n",
      "Epoch 14/2000\n",
      "30/30 [==============================] - 0s 233us/step - loss: 28659.4590\n",
      "Epoch 15/2000\n",
      "30/30 [==============================] - 0s 300us/step - loss: 28616.3535\n",
      "Epoch 16/2000\n",
      "30/30 [==============================] - 0s 266us/step - loss: 28573.2422\n",
      "Epoch 17/2000\n",
      "30/30 [==============================] - 0s 233us/step - loss: 28530.1445\n",
      "Epoch 18/2000\n",
      "30/30 [==============================] - 0s 199us/step - loss: 28487.0566\n",
      "Epoch 19/2000\n",
      "30/30 [==============================] - 0s 300us/step - loss: 28443.9941\n",
      "Epoch 20/2000\n",
      "30/30 [==============================] - 0s 366us/step - loss: 28400.9551\n",
      "Epoch 21/2000\n",
      "30/30 [==============================] - 0s 500us/step - loss: 28357.9414\n",
      "Epoch 22/2000\n",
      "30/30 [==============================] - 0s 232us/step - loss: 28314.9668\n",
      "Epoch 23/2000\n",
      "30/30 [==============================] - 0s 261us/step - loss: 28272.0293\n",
      "Epoch 24/2000\n",
      "30/30 [==============================] - 0s 333us/step - loss: 28229.1289\n",
      "Epoch 25/2000\n",
      "30/30 [==============================] - 0s 327us/step - loss: 28186.2676\n",
      "Epoch 26/2000\n",
      "30/30 [==============================] - 0s 295us/step - loss: 28143.4434\n",
      "Epoch 27/2000\n",
      "30/30 [==============================] - 0s 363us/step - loss: 28100.6738\n",
      "Epoch 28/2000\n",
      "30/30 [==============================] - 0s 267us/step - loss: 28057.9414\n",
      "Epoch 29/2000\n",
      "30/30 [==============================] - 0s 267us/step - loss: 28015.2598\n",
      "Epoch 30/2000\n",
      "30/30 [==============================] - 0s 333us/step - loss: 27972.6250\n",
      "Epoch 31/2000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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      "Epoch 1998/2000\n",
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      "Epoch 1999/2000\n",
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      "Epoch 2000/2000\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x20c4a0d28d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x, y, epochs=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights('a.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 88.528  ],\n",
       "       [105.58407],\n",
       "       [122.64013],\n",
       "       [139.69621],\n",
       "       [156.75229],\n",
       "       [173.80835],\n",
       "       [190.86443],\n",
       "       [207.92049],\n",
       "       [224.97655],\n",
       "       [242.03262],\n",
       "       [259.08868],\n",
       "       [276.14474],\n",
       "       [293.2008 ],\n",
       "       [310.25687],\n",
       "       [327.31293],\n",
       "       [344.36902],\n",
       "       [361.42508],\n",
       "       [378.48114],\n",
       "       [395.5372 ],\n",
       "       [412.59326],\n",
       "       [429.64935],\n",
       "       [446.70544],\n",
       "       [463.76147],\n",
       "       [480.81757],\n",
       "       [497.8736 ],\n",
       "       [514.92975],\n",
       "       [531.9858 ],\n",
       "       [549.0419 ],\n",
       "       [566.0979 ],\n",
       "       [583.154  ]], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20c67821e10>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.scatter(x, model.predict(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
